Method for evaluating athlete, system and device performing the same

ABSTRACT

Disclosed are an athlete evaluation method and a system and device for performing the same. The athlete evaluation method may include acquiring location data of a plurality of participants, computing a reference location of a specific sport participant on the basis of location data of other sport participants, generating relative location data of the specific participant from the location data of the specific participant in consideration of the reference location, generating heatmap data of the specific participant on the basis of the relative location data, and computing a performance index of the specific participant.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/991,983, filed on Mar. 19, 2020, thedisclosure of which is incorporated herein by reference in its entirety.

This application claims priority to and the benefit of Korean PatentApplication No. 10-2020-0159357, filed on Nov. 24, 2020, the disclosureof which is incorporated herein by reference in its entirety.

BACKGROUND 1. Field of the Invention

The present invention relates to a method of evaluating an athlete and asystem for performing the same, and more particularly, to a method ofperforming athlete evaluation using athlete-related data and an analysisapparatus for performing the same.

2. Discussion of Related Art

Conventionally, in the sport industry, it is common that the evaluationof athletes is subjectively evaluated by a sport evaluation agency. Inthe case of the conventional athlete evaluation method, an objectiveevaluation was difficult because criteria for athlete evaluation wereambiguous and not uniform.

Therefore, in the modern sport industry, there is an increasing demandfor an Electronic Performance Tracking System (“EPTS”) which canobjectively evaluate athletes using machines.

However, in the case of the conventional EPTS, only indictors forpartial and fragmentary individual athletic abilities are provided forathletes. Therefore, there is a need to evaluate an athlete bycomprehensively and consiliently analyzing athlete-related data acquiredby the EPTS and performing a test on the development of the athlete'sability according to the evaluation result.

SUMMARY OF THE INVENTION

The present invention is directed to providing a method of evaluating anathlete in consideration of a unique feature of an athlete in acorresponding sport in order to promote a more accurate and objectiveevaluation by reflecting the characteristics of the corresponding sportwhen evaluating an athlete, and a system and device for performing thesame.

The present invention is directed to providing a method of processinglocation data and at least one piece of dynamic data related to anathlete using data suitable for the evaluation of a corresponding sportin order to increase the accuracy or reliability of the evaluation ofthe athlete, and a system and device for performing the same.

The present invention is directed to providing a method of acquiring anobjective evaluation index of a specific player who plays a sport amonga plurality of players who play the corresponding sport, and a systemand device for performing the same.

The present invention is directed to providing a method of processingsport data related to a target player to correct the sport data to beappropriate for the evaluation of a specific sport performed by thetarget player, and a system and device for performing the same.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent to those of ordinary skill in theart by describing exemplary embodiments thereof in detail with referenceto the accompanying drawings, in which:

FIG. 1 schematically shows an example of an analysis system according toan embodiment;

FIG. 2 is a block diagram schematically showing an analysis system 100according to an embodiment;

FIG. 3 is a block diagram of a data acquisition device according to anembodiment;

FIG. 4 is a block diagram of a data analysis device according to anembodiment;

FIG. 5 is a flowchart of a data analysis method that is performed by adata analysis device according to an embodiment;

FIG. 6 is a flowchart showing a method of a data analysis devicepre-processing sport data according to an embodiment;

FIG. 7 shows an example of heatmap data labeled with the characteristicsof a sport object according to an embodiment;

FIG. 8 shows a location heatmap data correction method performed by adata analysis device according to an embodiment;

FIG. 9 shows an example of an uncorrected location heatmap in twodifferent matches for an athlete according to an embodiment;

FIG. 10 shows an example of reference location data andspecific-time-point location data in two different matches for anathlete according to an embodiment.

FIG. 11 shows corrected specific-time-point location data in twodifferent matches for an athlete according to an embodiment;

FIG. 12 shows relative location heatmap data obtained by correctinglocation heatmap data of FIG. 8 according to an embodiment;

FIG. 13 is a schematic flowchart of a method of a data analysis device2000 correcting dynamic heatmap data;

FIG. 14 shows an example of uncorrected dynamic heatmap data accordingto an embodiment;

FIG. 15 shows an example of corrected dynamic heatmap data according toan embodiment;

FIG. 16 shows another example of corrected dynamic heatmap dataaccording to an embodiment;

FIG. 17 shows another example of corrected dynamic heatmap dataaccording to an embodiment;

FIG. 18 is a flowchart showing an exemplary method of a data analysisdevice analyzing data using corrected heatmap data;

FIG. 19 is a flowchart showing another example of a method of a dataanalysis device analyzing data using corrected heatmap data;

FIG. 20 is related to a method of a data analysis device extractingprincipal components from a plurality of pieces of heatmap dataaccording to an embodiment;

FIG. 21 is a flowchart showing a method of a data analysis deviceprocessing principal components acquired from a plurality of pieces ofheatmap data according to an embodiment;

FIG. 22 shows a method of a data analysis device extracting a principalcomponent in consideration of the characteristics of an athleteaccording to an embodiment;

FIG. 23 shows a method of a data analysis device clustering sportobjects by characteristic on the basis of a plurality of feature indicesaccording to an embodiment;

FIG. 24 shows exemplary location heatmap data-related common principalcomponents according to an embodiment;

FIG. 25 shows exemplary dynamic heatmap data-related common principalcomponents according to an embodiment;

FIG. 26 shows an example of an acquired feature index according to anembodiment;

FIG. 27 shows an exemplary method of a data analysis device evaluating atarget player according to an embodiment;

FIG. 28 shows an example of a team evaluation method performed by a dataanalysis device according to an embodiment; and

FIG. 29 shows an example of a target entity evaluation method performedby a data analysis device according to an embodiment.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The above objects, features, and advantages of the present inventionwill become more apparent from the following detailed description takenin conjunction with the accompanying drawings. However, since thepresent invention may be variously modified and have several exemplaryembodiments, specific exemplary embodiments will be shown in theaccompanying drawings and described in detail.

In the figures, the thickness of layers and regions is exaggerated forclarity. Also, when it is mentioned that an element or layer is “on”another element or layer, the element or layer may be formed directly onthe other element or layer, or a third element or layer may beinterposed therebetween. Like reference numerals refer to like elementsthroughout the specification. Further, like reference numerals will beused to designate like elements within the same scope shown in thedrawings of the embodiments.

Detailed descriptions about well-known functions or configurationsassociated with the present invention will be omitted in order not tounnecessarily obscure the subject matter of the present invention. Also,ordinal numbers (e.g., first, second, etc.) used in the followingdescription are merely identification symbols for distinguishing oneelement form another element.

The suffixes “module” and “unit” for elements used in the followingdescription are given or used interchangeably only for the ease ofwriting this specification, and thus do not themselves have distinctmeanings or roles.

The present disclosure relates to a method for evaluating a targetentity by analyzing data acquired from at least one or more sportobjects, and a system for performing the same.

Here, a sport may be interpreted in various ways as all sports having atarget capable of acquiring data and an object capable of analyzing andevaluating the acquired data. That is, the term “sport” used hereinrefers to a comprehensive concept of sports, which includes, forexample, individual sports and team sports, ball sports and non-ballsports, sports with sport instruments and sports without sportinstruments, and professional sports and amateur sports. However, forconvenience of description, the following description will focus onsoccer, but the spirit of the present disclosure is not limited tosoccer.

In the following drawings, an analysis system according to an embodimentwill be schematically described, and terms to be used in the followingdescription of the present disclosure will be summarized with referenceto the drawings. However, it is to be understood that the terms usedherein are for convenience of description and the spirit of the presentdisclosure is not limited by these terms.

FIG. 1 schematically shows an example of an analysis system according toan embodiment.

Referring to FIG. 1 , an evaluation system according to an embodimentmay acquire data related to a sport object, analyze the acquired data,and evaluate a target entity.

Here, the term “sport object” may refer to any object that is associatedwith a sport and that can be an object of data acquisition related tothe corresponding sport. That is, the sport object may be a player 1 whoplays the corresponding sport and may include specific objects used inthe corresponding sport. For example, the sport object may include aball 2, a goalpost 3, or a line drawn on a pitch G.

Also, the term “target entity” may refer to an object that is associatedwith a corresponding sport and that be analyzed and evaluated by usingdata acquired from a sport object. Here, the target entity may or maynot be included in the sport object. That is, the analysis methodaccording to an embodiment may acquire data from at least one sportobject including the target entity and evaluate the target entity or mayacquire data from at least one sport object excluding the target entityand evaluate the target entity.

As an example, the target entity may indicate a player 1 who plays thecorresponding sport. Here, the player 1, who is subject to evaluationthrough the system 100 according to an embodiment, is referred to as a“target player T.”

Also, the target entity may be a combination of multiple players 1 whoplay the corresponding sport. For example, the target entity mayindicate a plurality of players 1 in the same position or may indicate aplurality of players 1 assigned the same mission.

Also, the target entity may be a team that plays the correspondingsport. Here, the target entity may refer to some or all of the team thatplays the corresponding sport. For example, the target entity may beclassified for each tactic of the team. More specifically, the targetentity may be classified for each role such as an offense group, adefense group, and a midfielder group.

Referring to FIG. 1 again, the acquisition of sport data according to anembodiment may be implemented through an EPTS.

Here, the EPTS may be implemented in a form in which a data acquisitiondevice 1000 is installed in a sport object. For example, the EPTS mayacquire sport object-related data on the basis of GPS 10 data of thesport object. As another example, the EPTS may acquire sportobject-related data on the basis of short-range communication(ultra-wideband (UWB)). As another example, the EPTS may be implementedbased on an inertial measurement unit (IMU) sensor such asaccelerometers and gyrometers. The data acquisition device 1000installed in the sport object may be generally implemented in the formof a wearable device but is not limited thereto.

Also, the EPTS may be implemented based on image data. For example, theEPTS may acquire data related to a sport object using image datacaptured by a camera 30 installed in a stadium.

Also, herein, the sport object-related data acquired through the dataacquisition device 1000 is referred to as “sport data.” The sport datamay include location data and dynamic data, which will be described indetail below.

FIG. 2 is a block diagram schematically showing an analysis system 100according to an embodiment.

Referring to FIG. 2 , the system 100 according to an embodiment mayinclude a data acquisition device 1000 and a data analysis device 2000.

According to an embodiment, the data acquisition device 1000 maytransmit acquired sport data to the data analysis device 2000. The dataacquisition device 1000 may directly transmit at least one piece ofsport data to the data analysis device 2000. Alternatively, the dataacquisition device 1000 may transmit at least one piece of sport data toa repeater 1500 first. When at least one piece of sport data isreceived, the repeater 1500 may transmit the received sport data to thedata analysis device 2000 collectively or separately.

Also, the data acquisition device 1000 may directly transmit the sportdata to the data analysis device 2000 or may pre-process the sport databefore the transmission. Alternatively, the pre-processing of the sportdata may be performed by the repeater 1500.

The data analysis device 2000 may analyze the sport data received fromthe data acquisition device 1000 or the repeater 1500 to evaluate thetarget entity. Also, the data analysis device 2000 may be implemented asa server that is connected to the data acquisition device 1000 over anetwork. Herein, the following description will focus on the dataanalysis device 2000 implemented as a device. However, this is only forconvenience of description, and it will be understood that the dataanalysis device 2000 may be implemented as a server that performs thesame function as described above. Also, a specific method performed bythe data analysis device 2000 will be described in detail below.

The elements of the analysis system 100 will be described in detailbelow with reference to the drawings.

FIG. 3 is a block diagram of a data acquisition device according to anembodiment.

Referring to FIG. 3 , a data acquisition device 1000 according to anembodiment may include at least one sensor module 1200, a firstcommunication module 1800, and a first controller 1002.

According to an embodiment, the first controller 1002 may acquire sportdata regarding an athlete through a sensor module 1200 and may transmitthe sport data acquired through the first communication module 1800 tothe data analysis device 2000.

The elements of the data acquisition device 1000 according to anembodiment will be described below.

According to an embodiment, the sensor module 1200 may include alocation sensor 1220 and/or a dynamic sensor 1240.

Here, the location sensor 1220 may measure location data of a sportobject. Specifically, the location sensor 1220 may measure the locationdata of the sport object and transmit the measured location data to thefirst controller 1002. Here, the location sensor 1220 may be implementedin various forms. As an example, the location sensor 1220 may beimplemented as a Global Navigation Satellite System (GNSS) antenna todetect a GNSS signal of the player 1. As another example, the locationsensor 1220 may be implemented as a UWB receiving module to detect a UWBsignal of the player 1.

Here, the dynamic sensor 1240 may measure dynamic data of the sportobject. Specifically, the dynamic sensor 1240 may measure the dynamicdata of the sport object and transmit the measured dynamic data to thefirst controller 1002. Here, the dynamic data may be data related to themovement of the sport object such as the velocity, acceleration, andjerk of the sport object. As an example, the dynamic sensor 1240 isimplemented as an accelerometer to measure the acceleration of theplayer 1. As another example, the dynamic sensor 1240 may be implementedas a gyrometer. As another example, the dynamic sensor 1240 may beimplemented as a magnetometer.

The first communication module 1800 according to an embodiment mayperform communication with an external device or an external server. Thedata acquisition device 1000 may perform data communication with therepeater 1200 or the data analysis device 2000 through the firstcommunication module 1800. For example, the data acquisition device 1000may use the first communication module 1800 to transmit the locationdata and/or the dynamic data of the athlete to the data analysis device2000 or the external device. Here, the data acquisition device 1000 maytransmit the sport data in real time or after a preset time has elapsed.Also, the data acquisition device 1000 may transmit the sport data of atleast one sport object individually or collectively.

Also, the first controller 1002 may acquire the dynamic data of theathlete measured through the dynamic sensor 1240 and may transmit thedynamic data acquired to the data analysis device 2000 through the firstcommunication module 1800.

Also, the first controller 1002 may acquire the location data of theathlete measured through the location sensor 1220, process the acquiredlocation data, and generate dynamic data.

The first controller 1002 may control the overall operation of the dataacquisition device 1000. For example, the first controller 1002 maygenerate a control signal to receive the location data from the locationsensor 1220 and then transmit the location data to the data analysisdevice 2000 through the first communication module 1800. As anotherexample, the first controller 1002 may generate a control signal toreceive the dynamic data from the dynamic sensor 1240 and transmit thedynamic data to the data analysis device 2000 through the firstcommunication module 1800.

Also, the first controller 1002 may generate additional data on thebasis of the sport data acquired from the sensor module 1200.Specifically, the first controller 1002 may generate the dynamic datausing the location data transmitted from the location sensor 1220. Also,the first controller 1002 may generate new dynamic data using thedynamic data received from the dynamic sensor 1240. As a specificexample, the first controller 1002 may generate velocity data bydifferentiating location data acquired at a plurality of time pointsover time.

Also, the first controller 1002 may transform the format of the locationdata or the dynamic data received from the location sensor 1220 or thedynamic sensor 1240.

The first controller 1002 may be implemented as a central processingunit (CPU) or a device similar to the CPU according to hardware,software, or a combination thereof. The first controller 1002 may beprovided in a hardware form of an electronic circuit for processing anelectrical signal to perform a control function. The first controller1002 may be provided in a software form of a program or a code fordriving a hardware circuit.

The data acquisition device 1000 may have a separate power supply unitor receive power from the outside in a wired or wireless manner and maya switch for controlling the power supply unit.

FIG. 4 is a block diagram of a data analysis device according to anembodiment.

Referring to FIG. 4 , a data analysis device 2000 may include a secondcontroller 2002, a memory 2200, and a second communication module 2800.

According to an embodiment, the second controller 2002 may acquirelocation data and/or dynamic data of an athlete from the dataacquisition device 1000 through the second communication module 2800,analyze the location data and/or the dynamic data using an analysisprogram stored in the memory 2200, and evaluate the athlete. Here, thedata analysis device 2000 may acquire sport data from the dataacquisition device 1000 in real time and evaluate the athlete in realtime.

The elements of the data analysis device 2000 according to an embodimentwill be described below.

The memory 2200 may store various kinds of information of the dataanalysis device 2000. Examples of the memory 2200 include a hard diskdrive (HDD), a solid state drive (SSD), a flash memory, a read-onlymemory (ROM), a random access memory (RAM), and the like.

Various kinds of data necessary for the operation of the data analysisdevice 2000 may be stored in the memory 2200 in addition to an operatingsystem for driving the data analysis device 2000 or a program foroperating the elements of the data analysis device 2000. For example, aprogram for processing the location data and/or the dynamic data of theathlete and a machine learning algorithm for analyzing the location dataand/or the dynamic data may be stored in the memory 2200.

The second communication module 2800 may perform communication with anexternal device or an external server. The data analysis device 2000 mayuse the second communication module 2800 to perform data communicationwith the data acquisition device 1000 or an external server. Forexample, the data analysis device 2000 may use the second communicationmodule 2800 to acquire data necessary to evaluate an athlete from thedata acquisition device 1000.

The second controller 2002 may be implemented as a central processingunit (CPU) or a device similar to the CPU according to hardware,software, or a combination thereof. The second controller 2002 may beprovided in a hardware form of an electronic circuit for processing anelectrical signal to perform a control function. The second controller2002 may be provided in a software form of a program or a code fordriving a hardware circuit.

The second controller 2002 may control the overall operation of the dataanalysis device 2000. For example, the second controller 2002 may load aprogram for processing and analyzing data from the memory 2200 andprocess and analyze data acquired from the data acquisition device 1000and may generate a control signal to provide a result of the processingand analysis to an external device or an external server through thesecond communication module 2800. The sport data analysis methodperformed by the data analysis device 2000 will be described in detailbelow.

Also, the data analysis device 2000 may further include a separateoutput unit for outputting a data analysis result. Here, the secondcontroller 2002 may process and analyze the data acquired from the dataacquisition device 1000 and generate a control signal for providing aresult of the processing and analysis through the output unit.

Also, the second controller 2002 may generate additional data on thebasis of the data acquired from the data acquisition device 1000.Specifically, the second controller 2002 may acquire dynamic data on thebasis of the location data of the athlete acquired from the dataacquisition device 1000 through the second communication module 2800. Asa more specific example, the second controller 2002 may acquire velocitydata of the athlete by differentiating time-dependent location data ofthe athlete acquired from the data acquisition device 1000.

The elements of the analysis system 100 according to an embodiment havebeen described above. A target entity analysis method implemented in theanalysis system 100 according to an embodiment will be described belowwith reference to the drawing.

FIG. 5 is a flowchart of a data analysis method that is performed by adata analysis device according to an embodiment.

Referring to FIG. 5 , the sport data analysis method according to anembodiment may include acquiring data from a sport object (S1000),pre-processing the acquired data (S1200), and analyzing thepre-processed data (S1400).

First, in the data acquisition step S1000, the system 100 may acquiresport data. In detail, the data acquisition device 1000 may acquiresport data from a sport object and transmit the acquired sport data tothe data analysis device 2000. The detailed operation of acquiring thesport data from the data acquisition device 1000 has been describedabove.

In the step of pre-processing the acquired data (S1200), the system 100may pre-process the sport data. Specifically, the data analysis device2000 may pre-process the sport data received from the data acquisitiondevice 1000. In detail, the second controller 2002 may pre-process thesport data acquired by the data acquisition device 1000 as input data ofan analysis algorithm. More specifically, the second controller 2002 maygenerate heatmap data for the sport data and process the generatedheatmap data as input data of the analysis algorithm. Here, thepre-processing of the sport data may be performed to reduce theinfluence of match-specific internal and external factors on the sportobject-related data or may be performed to extract data that ismeaningful in the analysis algorithm. The operation of pre-processingthe acquired data (S1200) will be described in detail below.

After the data pre-processing step S1200, the system 100 may analyze thepre-processed sport data (S1400). Specifically, the second controller2002 of the data analysis device 2000 inputs the pre-processed sportdata to the analysis algorithm and evaluates the target entity on thebasis of an output result of the analysis algorithm. The evaluation ofthe analysis algorithm and the target entity performed in the dataanalysis step S1400 will be described in detail below.

The sport data analysis method performed by the system 100 according toan embodiment has been outlined above.

The sport data pre-processing performed by the data analysis device 2000has been described in detail below with reference to FIGS. 6 to 16 .

FIG. 6 is a flowchart showing a method of a data analysis devicepre-processing sports data according to an embodiment.

Referring to FIG. 6 , the sport data pre-processing method performed bythe data analysis device according to an embodiment may includegenerating heatmap data (S2200) and correcting heatmap data (S2400).

First, in the step of generating the heatmap data (S2200), the dataanalysis device 2000 may generate heatmap data for the sport data. Here,the heatmap data may refer to data representing a frequency distributionof the sport data for a predetermined time. In other words, the heatmapdata may refer to a set of sport data during a predetermined time. Also,a simple visualization of heatmap data may be expressed as a “heatmap”,and these terms may be used interchangeably.

Here, the predetermined time may be variously set. As an example, thepredetermined time may be at least a portion of the total time of thesport match.

Specifically, the predetermined time may be a total play time for whichan athlete plays the corresponding sport. Specifically, thepredetermined time may be a play time for which an athlete plays thecorresponding sport while performing a specific position. Also, thepredetermined time may be a time for which an athlete plays thecorresponding sport while performing a specific role. This will bedescribed in detail later.

Also, according to an embodiment, sport heatmap data may include“location heatmap data” and “dynamic heatmap data.”

Here, the term “location heatmap data” may refer to a frequencydistribution of data on locations that the player 1 occupies in astadium for a predetermined time. Here, the location heatmap data mayinclude a plurality of cells, and the cells may correspond to a stadiumwhere the player 1 plays the sport.

Also, the term “dynamic heatmap data” may refer to a frequencydistribution of dynamic data on the movement of the player 1 for apredetermined time. As an example, velocity heatmap data may be formedin a matrix with two dimensions, which may indicate a velocity vector inan offense/defense direction of the stadium and a velocity vector in adirection orthogonal to the offense/defense direction. As anotherexample, acceleration heatmap data may also be formed in a matrix withtwo dimensions, which may indicate an acceleration vector in theoffense/defense direction of the stadium and a velocity vector in adirection orthogonal to the offense/defense direction.

After the heatmap data generation step S2200, the data analysis device2000 may correct the generated heatmap data (S2400).

In the heatmap data correction step S2400, the data analysis device 2000may correct the heatmap data so as to reduce the influence ofmatch-specific internal and external factors related to the sport objectand improve the accuracy of the analysis algorithm. The heatmap datacorrection method will be described in detail below.

Also, according to an embodiment, the data analysis device 2000 mayperform labeling with the athlete's characteristics when generating theheatmap data.

FIG. 7 shows an example of heatmap data labeled with the characteristicsof a sports object according to an embodiment.

Referring to FIG. 7 , the data analysis device 2000 may generate sportheatmap data labeled with the characteristics of the sport object.Specifically, the second controller 2002 may generate sport heatmapdata, analyze the generated sport heatmap data, determine thecharacteristics of an athlete, and perform labeling with the determinedcharacteristics of the athlete. Here, the sport data may be labeled withthe characteristics of the athlete and transmitted to the data analysisdevice 2000 by the data acquisition device 1000. In this case, thesecond controller 2002 may label the generated sport heatmap data withthe characteristics of the athlete received from the data acquisitiondevice 1000. Also, here, the determining of the characteristics of theathlete may be omitted. Here, the labeling may be implemented by addingan identifier reflecting the player's characteristics to the heatmapdata.

Also, the characteristics of the sport object including the player mayrefer to all properties related to the sport or related to the sportobject. For example, the characteristics of the sport object may betactical characteristics in a sport match. For example, thecharacteristics of the sport object may include a position played by thesport object, a role performed in a match by the sport object, and thelike. Also, the characteristics of the sport object may be individualcharacteristics of an athlete. For example, the characteristics of thesport object may include the athlete's age, gender, and the like. Also,the characteristics of the sport object may include a sport level. Forexample, the characteristics of the sport object may include the levelof a league in which the sport object plays, the level of a competitionin which the sport object has participated, whether it is a friendlymatch, and the like. Also, player characteristic information may beprestored in the memory 2200.

An example will be described with reference to FIG. 7 .

FIG. 7 shows heatmap data reflecting the characteristics of the sameplayer. Specifically, location heatmap data reflecting a firstcharacteristic and location heatmap data reflecting a secondcharacteristic are shown.

Here, as an example, the first characteristic may refer to a firstposition performed by the player. Also, as an example, the secondcharacteristic may refer to a second position different from the firstposition performed by the player. In detail, the location heatmap shownin FIG. 7 may represent location data regarding the first positionperformed by the player in the match for a specific time and locationdata regarding the second position performed for another specific time.Also, here, the location heatmap related to the first position may belabeled with a first identifier, and the location heatmap related to thesecond position may be labeled with a second identifier. Here, the firstidentifier and the second identifier may include information regardingthe first position and information regarding the second information,respectively.

A heatmap data correction method performed by the data analysis devicewill be described in detail below with reference to the drawing.

First, the correction of the location heatmap data performed by the dataanalysis device 2000 will be described with reference to FIGS. 8 to 12 .Then, the correction of the dynamic heatmap data performed by the dataanalysis device 2000 will be described with reference to FIGS. 13 to 17.

FIG. 8 shows a location heatmap data correction method performed by adata analysis device according to an embodiment.

Referring to FIG. 8 , the method of correcting the location heatmap datamay include determining a reference location on the basis of locationdata of a sport object (S2420), determining a relative location of atarget entity on the basis of the reference location (S2440), andgenerating a corrected location heatmap on the basis of the relativelocation (S2460).

In the step of determining the reference location (S2420), first, thedata analysis device 2000 may determine the reference location on thebasis of the location data of the sport object. Specifically, the secondcontroller 2002 may acquire reference location data, which is areference for correcting location data of the target entity, on thebasis of location data of at least one sport object. Here, thedetermining of the reference location will be described in detail below.

In the step of determining the relative location of the target entity(S2440) after the reference location determination step S2420, the dataanalysis device 2000 may determine the relative location of the targetentity on the basis of the reference location. Specifically, the secondcontroller 2002 may determine the relative location of the target entityusing the location data of the target entity and the reference locationderived from at least one sport object. Here, the relative location ofthe target entity may vary depending on the predetermined referencelocation. Here, the method of determining the relative location will bedescribed in detail below.

When the relative location of the target entity is determined, the dataanalysis device 2000 may generate corrected heatmap data (S2460).Specifically, the second controller 2002 may generate corrected locationheatmap data on the basis of the relative location of the target entity.

An example of the location heatmap data generated by the data analysisdevice 2000 according to an embodiment will be described first belowwith reference to FIG. 9 , and a method in which the location heatmapdata is corrected by the data analysis device 2000 shown in FIG. 8 willbe described with reference to FIGS. 9 to 12 .

FIG. 9 shows an example of an uncorrected location heatmap in twodifferent matches for an athlete according to an embodiment.

Referring to FIG. 9 , location heatmap data for one athlete playing twodifferent matches is visualized. FIG. 9 shows that the athlete mainlyoccupies the right side of a stadium with respect to the offensedirection while playing each of the sport matches. Also, a locationoccupancy frequency is visualized to be high on the right side, wherethe athlete has a high location occupancy frequency on a pitch duringthe match, and a location occupation frequency is visualized to be lowon the left side, where the athlete has a low location occupancyfrequency. Also, a location occupancy frequency is visualized to bemedium in a pitch center region having a lower location occupancyfrequency than the right side and having a higher location occupancyfrequency than the left side.

In the following description, for convenience of description, the lengthdirection of the stadium may be referred to as an offense/defensedirection, and the width direction of the stadium may be referred to asa transverse direction. It will be understood that these terms may beused interchangeably.

As shown, the location heatmap data may indicate the total locationoccupancy frequency of the sport object during each match but may notreflect information regarding match-specific internal and externalfactors.

For example, as shown in FIG. 8 , when an athlete or a team to which anathlete belongs uses a tactic with a defense tendency, the locationheatmap data of the athlete may be biased toward the defense direction.As another example, when an athlete or a team to which an athletebelongs uses a tactic with an offense tendency, the location heatmapdata of the athlete may be biased toward the offense direction. Inaddition, for various reasons, the location heatmap data of the athletemay not reflect the match-specific internal and external factors.

As an example, when the form of the athlete's location heatmap datachanges according to the team's tactical tendency, the location heatmapdata does not fully reflect the athlete's personal tendencies. Thus, theaccuracy of the evaluation of the athlete based on the location heatmapdata may be decreased.

Accordingly, in order to improve the accuracy of the evaluation of theathlete based on the location heatmap data, there is a need topre-process the location data by reflecting information on sportmatch-related internal and external factors.

FIGS. 10 to 12 show a method of the data analysis device generatingcorrected location heatmap data according to an embodiment.

FIG. 10 shows an example of reference location data andspecific-time-point location data in two different matches for anathlete according to an embodiment.

Referring to FIG. 10 , the data analysis device 2000 may determine thereference location data in order to correct location heatmap data.Specifically, the second controller 2002 may determine the referencelocation data on the basis of location data of at least one sportobject.

Here, the reference location data may be variously set to attenuate thematch-specific internal factors. As an example, the reference locationdata may be determined based on the location data of the at least onesport object. As a specific example, the reference location data may bean average location of at least two players of a team including a targetplayer. As another specific example, the reference location data may bean average location of all players of the team. Also, the referencelocation data may be an average location of some players of the team.Here, the target player may or may not be included in calculating theaverage location.

As another example, the reference location data may be location data ofa specific sport object. In detail, the reference location data may belocation data of a ball. As another example, the reference location datamay be location data of at least one referee.

In more detail with reference to the drawings, FIG. 10 shows locationdata of all athletes of the team including the target player. First,location data for a first match shows that the team including the targetplayer has location data biased to defense. Also, location data for asecond match shows that the team including the target player haslocation data biased to offense.

In the first match, an average location of all players of a teamincluding a target player is shown. In this case, the distance in theoffense/defense direction between the average location and the locationof the target player in the first match may be expressed as ΔX1. Here,the average location was used as reference location data for correctinglocation heatmap data of the first match.

Also, in the second match, an average location of all players of a teamincluding a target player is shown. In this case, the distance in theoffense/defense direction between the average location and the locationof the target player in the second match may be expressed as ΔX2. Here,the average location was used as reference location data for correctinglocation heatmap data of the second match.

The method of determining reference location data has been describedabove. A method of the data analysis device 2000 acquiring correctedlocation data using reference location data and/or original locationdata will be described below.

FIG. 11 shows corrected specific-time-point location data in twodifferent matches of an athlete according to an embodiment.

Referring to FIG. 11 , the second controller 2002 may generate correctedlocation data on the basis of acquired reference location data.Specifically, the second controller 2002 may generate corrected locationdata on the basis of data obtained by combining the reference locationdata and location data of a target player. The corrected location datais hereinafter referred to as “relative location data.” Also, heatmapdata generated based on relative location data is hereinafter referredto as “relative location heatmap data.”

Here, there may be various methods of combining the reference locationdata and the location data of the target player. As an example, thecombined location data may be location data generated based on a valueobtained by arithmetically applying a coordinate value of the locationdata of the target layer to a coordinate value of the reference locationdata.

Also, here, when the reference location data and the location data ofthe target player are combined, some or all of the location data may beused. For example, as shown in FIGS. 10 and 11 , the second controller2002 may generate the relative location data using only anoffense/defense direction coordinate value of the location data. Morespecifically, the second controller 2002 may generate the relativelocation data using ΔX1, which is the difference between offense/defensedirection coordinate values of the reference location data and thelocation data of the target player, in the case of the first match andmay generate the relative location data using ΔX2, which is found in asimilar way, in the case of the second match.

Also, here, the second controller 2002 may generate corrected locationdata on the basis of a predetermined value. Specifically, the secondcontroller 2002 may generate the relative location data byarithmetically applying a predetermined value to the location data ofthe target player or data obtained by combining the location data of thetarget player and the reference location data. The predetermined valuemay be stored in the memory 2200. Here, the predetermined value may bevariously set. As an example, the predetermined value may be at least aportion of an offense/defense direction length value of a pitch in whichan athlete has played. As another example, the predetermined value maybe at least a portion of a transverse direction length value of a pitchin which an athlete has played.

That is, according to an embodiment of the present disclosure, thesecond controller 2002 may generate relative location data on the basisof data obtained by combining the reference location data and thelocation data of the target entity, generate the relative location dataon the basis of a predetermined value, or generate the relative locationdata by combining the above two schemes.

A detailed example will be described below with reference to thedrawing.

For the relative location of the target player of the first match whichis generated by the second controller 2002, a coordinate obtained byadding a predetermined proportion of the total length of the pitch inthe offense/defense direction to the distance ΔX1 in the offense/defensedirection between the average location 8′ and the location 7′ of thetarget player in the first match may be computed as a corrected location9 of the target player in the first match. Here, among the location dataof the target player, the coordinate value in the transverse directionof the pitch is not changed.

As another example, for the relative location of the target player ofthe second match, a coordinate obtained by adding a predeterminedproportion of the total length of the pitch in the offense/defensedirection to the distance ΔX2 in the offense/defense direction betweenthe average location and the location data of the target player in thesecond match may be computed as a corrected location of the targetplayer in the second match. As in the first match, among the locationdata of the target player, the coordinate value in the transversedirection of the pitch is not changed.

In this embodiment, the origin of the coordinate system representing thelocation data is set as the end of the pitch in the defense direction.However, it will be appreciated that there are various methods ofsetting the origin of the coordinate system and that the spirit of thepresent disclosure is not limited to this embodiment.

Relative location heatmap data will be described based on relativelocation data at each corrected time point will be described below withreference to the drawing.

FIG. 12 shows relative location heatmap data of the location heatmapdata of FIG. 9 according to an embodiment.

In detail, FIG. 12 is an example of the relative location heatmap dataof the location heatmap data of FIG. 9 generated through the processesof FIGS. 10 and 11 .

Referring to FIGS. 9 and 12 , compared to uncorrected location heatmapdata of a target player (see FIG. 9 ), corrected location heatmap data(see FIG. 12 ) shows a reduced deviation between the first game and thesecond game well.

With the analysis method according to an embodiment, by minimizing theinfluences of various factors that may occur in each match in which atarget player plays, it is possible to accurately analyze and evaluatelocation data related to the target player.

Also, the method of correcting the location heatmap data of the athletehas been mainly described above, but embodiments of the presentdisclosure are not limited thereto.

As an example, the data analysis device 2000 may pre-process locationheatmap data obtained by combining location heatmap data of a pluralityof players. That is, the second controller 2002 may pre-process alllocation heatmap data for a target entity as well as a location heatmapof a player. For example, when the target entity is a team, the secondcontroller 2002 may pre-process data obtained by combining locationheatmap data of all players who are identified as one team. When thetarget entity includes a plurality of players who perform somepositions, the second controller 2002 may pre-process data obtained bycombining location heatmap data of the plurality of players.

In this way, the data analysis device 2000 may pre-process all locationheatmap data related to the target entity.

A method of pre-processing dynamic data acquired from an athlete will bedescribed below with reference to the drawing.

First, a method of the data analysis device 2000 correcting dynamicheatmap data will be schematically described with reference to thedrawing.

FIG. 13 is a schematic flowchart of a method of a data analysis device2000 correcting dynamic heatmap data.

Referring to FIG. 13 , the method of correcting dynamic heatmap data mayinclude determining a criterion for extracting dynamic data to beanalyzed (S2422) and generating a corrected dynamic heatmap on the basisof the determined criterion (S2442).

First, in the criterion determination step S2422, the data analysisdevice 2000 may determine the criterion in consideration of thecharacteristics of a target entity.

Here, the characteristics of the target entity may vary. For example,the characteristics of the target entity may include the level of aleague in which the target entity plays, the age of the target entity,the career of the target entity, a competition in which the targetentity participates, etc.

In detail, the second controller 2002 may set the criterion of thedynamic heatmap data for evaluating the target entity on the basis ofprestored information regarding the characteristics of at least onetarget entity.

Also, the criterion for correcting the dynamic heatmap data may bedetermined in various forms. For example, the criterion for correctingthe dynamic heatmap data may be a specific speed value, a specificacceleration value, a specific jerk value, a specific direction, or thelike and may also be set to a combination or range thereof.

Here, the method of determining the criterion for extracting the dynamicdata to be analyzed will be described in detail below.

When the criterion for analyzing the dynamic data is set (S2422), thesecond controller 2002 may generate a corrected dynamic heatmap data onthe basis of the set criterion (S2442). Examples of the correcteddynamic heatmap data will be described in detail below.

The method of the data analysis device 2000 determining the criterionfor extracting the dynamic data, which is subject to the analysis methodaccording to an embodiment, and examples of the corrected dynamicheatmap data corrected according to the criterion will be describedbelow with reference to the drawings.

FIG. 14 shows an example of dynamic heatmap data according to anembodiment.

Referring to FIG. 14 , the data analysis device 2000 may generatedynamic heatmap data. For example, velocity heatmap data, which is a setof velocity data of an athlete during a match, is shown in the dynamicheatmap data shown in the drawing. Specifically, the size value of eachpiece of the shown velocity data represents the speed of the athlete ata specific time point, and a direction vector of each piece of velocitydata may represent the movement direction of the athlete at a specifictime point.

Here, the velocity heatmap data may be acquired based on theabove-described location heatmap data. As an example, the velocityheatmap data may be acquired by differentiating the location heatmapdata.

For example, FIG. 14 shows the distribution of the velocity of movementperformed by the athlete during a match. In detail, it is shown that thecorresponding athlete has a higher movement frequency in theoffense/defense direction than in the transverse direction and performsa movement with the maximum speed of about 35 km/h.

In the drawing, the corresponding velocity heatmap data shows that theathlete has similar dynamic frequency distributions in a low-speedsection and a high-speed section. In this case, since the only data thatcan be meaningfully acquired from the dynamic heatmap data in terms ofevaluation of the athlete's ability is information about the direction,it is necessary to process the dynamic heatmap data to extractmeaningful velocity data.

A method of the data analysis device 2000 correcting dynamic heatmapdata will be described below with reference to the drawing.

FIGS. 15 to 17 show various examples of dynamic heatmap data obtained bycorrecting the dynamic heatmap data of FIG. 14 according to anembodiment.

Referring to FIGS. 15 to 17 , the data analysis device 2000 may processdynamic heatmap data to generate corrected dynamic heatmap data.Specifically, the second controller 2002 may process dynamic heatmapdata according to a predetermined criterion to correct the dynamicheatmap data.

Here, the predetermined criterion may be determined in consideration ofthe characteristics of a target entity such that the target entity'sability is accurately evaluated.

As an example, the predetermined criterion may be set in considerationof a sport that is played by the target entity. This is because forexample, in the case of sports such as soccer, motions that are lessrelated to ability evaluation, such as walking or jogging at low speeds,occur frequently depending on the location of a ball and motions thatcan evaluate the ability or tactical movement of an athlete occursmainly occur in a sprint section or during high-speed movements.Accordingly, the predetermined criterion in sports such as soccer may bethe magnitude of speed or the magnitude of acceleration.

Also, the predetermined criterion may be set in consideration of theability level of the target entity. Here, various factors may becomprehensively considered in considering a player's ability level. Forexample, the factors may include the age or gender of a player, thelevel of a sport match, a friendly match, or a competition. As describedabove as an example, this is because the target entity may not beaccurately evaluated when the same criterion is applied to a youthplayer and an adult player or a high-level professional match or arelatively low-level of professional match in order to uniformly extractdynamic data.

Various criteria that may be set in processing dynamic heatmap data anddynamic heatmap data that is corrected according to a correspondingcriterion will be exemplarily described below with reference to thedrawings.

FIG. 15 shows an example of corrected dynamic heatmap data according toan embodiment.

Referring to FIG. 15 , the predetermined criterion may be set based onspeed. Specifically, the second controller 2002 may generate velocityheatmap data on the basis of velocity data with a speed greater than orequal to a threshold value.

Here, the predetermined criterion may be determined based onpre-acquired dynamic data of a plurality of players 1. As an example,the predetermined criterion may be determined based on a result ofanalyzing dynamic data pre-acquired from a plurality of players who areactive in the level of a match in which a target entity plays.

For example, when it is determined that velocity data greater than orequal to 15 km/h is meaningful in consideration of the level of a leaguein which the target entity plays and an age-specific level of the targetentity, the predetermined criterion may be a speed of 50 km/h. That is,the second controller 2002 may generate velocity heatmap data includingonly velocity data with a speed greater than or equal to thepredetermined criterion and may evaluate the target entity on the basisof the generated velocity heatmap data.

FIG. 16 shows another example of corrected dynamic heatmap dataaccording to an embodiment.

Referring to FIG. 16 , the predetermined criterion may be variously setwithin a numerical range included in dynamic data acquired from anathlete. As an example, when velocity data within a predetermined speedrange of an athlete is used for data analysis, the predeterminedcriterion may be the speed range of the athlete.

FIG. 17 shows another example of corrected dynamic heatmap dataaccording to an embodiment.

Referring to FIG. 17 , when velocity data within a predetermineddirection range of an athlete is used for data analysis, thepredetermined criterion may be a specific direction range of themovement of the athlete. That is, in the case of a sport where thedirection of the movement performed by the athlete is more importantthan the speed thereof, the predetermined criterion may be a specificdirection or a specific direction range in which the athlete plays.

The correction of the speed heatmap data has been described above withreference to the drawing, but it will be understood that similar methodsmay be performed to correct acceleration heatmap data or jerk heatmapdata.

Also, the method of correcting the dynamic heatmap data of the athletehas been mainly described above. Similar to the correction of thelocation heatmap data, however, embodiments of the present disclosureare not limited thereto.

As an example, the data analysis device 2000 may pre-process dynamicheatmap data obtained by combining dynamic heatmap data of a pluralityof players. That is, the second controller 2002 may pre-process alldynamic heatmap data for a target entity in combination or individuallyas well as a dynamic heatmap of a player. For example, when the targetentity is a team, the second controller 2002 may pre-process dataobtained by combining dynamic heatmap data of all players who areidentified as one team. When the target entity includes a plurality ofplayers who perform a specific position, the second controller 2002 maypre-process data obtained by combining dynamic heatmap data of theplurality of players.

In this way, the data analysis device 2000 may pre-process all dynamicheatmap data related to the target entity.

The method of the data analysis device 2000 correcting sport heatmapdata has been described above.

A method of the data analysis device 2000 analyzing corrected heatmapdata will be described below with reference to the drawing.

According to an embodiment, the data analysis device 2000 may performdata analysis using corrected heatmap data. Specifically, the secondcontroller 2002 inputs the corrected heatmap data to the analysisalgorithm and evaluates the target entity on the basis of an outputresult of the analysis algorithm.

Here, the data analysis device 2000 according to an embodiment may usevarious analysis algorithms.

As an example, the data analysis algorithm may be provided in the formof a machine learning model. A representative example of the machinelearning model includes a dimension reduction technique that converts asample in a high-dimensional space into a low-dimensional space whilepreserving the variance of data as much as possible. Here,representative examples of the dimension reduction technique includeprincipal component analysis (PCA), support vector machine (SVM), andnon-negative matrix decomposition (NMF), and the like. In addition,various machine learning techniques, such as the k-nearest neighboralgorithm (KNN) and the random forest, may be used, and an ensemble formof the aforementioned techniques or various combinations thereof may beused as the analysis algorithm according to an embodiment.

In addition, the machine learning model according to an embodiment maybe provided in the form of an artificial neural network. Arepresentative example of the artificial neural network is a deeplearning-based artificial neural network including an input layer, anoutput layer, and a hidden layer that processes data between the inputlayer and the output layer. However, the present invention is notlimited thereto, and various forms of artificial neural networks mayalso be used.

Furthermore, the analysis algorithm in this disclosure is notnecessarily limited to the machine learning model. That is, the analysisalgorithm may include various determination/decision algorithms otherthan the machine learning model.

Accordingly, it should be noted in advance that the analysis algorithmin this specification should be interpreted in a comprehensive senseincluding all types of algorithms capable of performing data analysisand athlete evaluation using data of athletes.

However, for convenience of description, an analysis algorithm using themachine learning model related to the dimension reduction technique willbe mainly described below. Thus, obviously, the analysis algorithm inthe present disclosure would not be limited to the dimension reductiontechnique-based machine learning model.

FIG. 18 is a flowchart showing an exemplary method of a data analysisdevice analyzing data using corrected heatmap data.

Referring to FIG. 18 , the data analysis method according to anembodiment may include extracting a principal component from heatmapdata (S3200), acquiring a feature index on the basis of the extractedprincipal component (S3400), and evaluating a target entity on the basisof the feature index (S3600).

First, in the method of extracting a principal component (S3200), thedata analysis device 2000 may extract a principal component of sportheatmap data. Here, a principal component is a vector included inheatmap data and refers to an eigenvector for reducing and re-expressinghigh-dimensional data distribution so as to analyze the distribution ofa plurality of pieces of sport data which is present as a vector. Also,here, a principal component may be a predetermined eigenvector which isextracted from pre-acquired sport heatmap data of a plurality of players1.

Specifically, the second controller 2002 may extract at least oneprincipal component from each piece of the sport heatmap data inconsideration of the type of sport data. For example, a location heatmapdata-related principal component and a dynamic heatmap data-relatedprincipal component may be different from each other. Hereinafter, aprincipal component extracted from location heatmap data is referred toas a “location-specific principal component,” and a principal componentextracted from dynamic heatmap data is referred to as a “dynamicprincipal component.”

Also, the second controller 2002 may extract at least one principalcomponent in consideration of the characteristics of a sport object.Here, the characteristics of the sport object may vary. Specifically,the characteristics of the sport object may be tactical characteristicsin a sport match. For example, the characteristics of the sport objectmay include a position played by the sport object, a role performed in amatch by the sport object, and the like. Also, the characteristics ofthe sport object may be individual characteristics of an athlete. Forexample, the characteristics of the sport object may include theathlete's age, gender, and the like. Also, the characteristics of thesport object may include a sport level. For example, the characteristicsof the sport object may include the level of a league in which the sportobject plays, the level of a competition in which the sport object hasparticipated, whether it is a friendly match, and the like. Theextraction of principal components in consideration of thecharacteristics of the sport object will be described in detail below.

Also, the principal component may reflect various elements of a sport.As an example, the principal component may reflect a tactical situationof a sport match. Specifically, the principal component may reflectwhether a match played by an athlete is offensive or defensive. Asanother example, the extracted principal component may reflect personaltendencies of the athlete. In addition to the previous example, it willbe understood that the principal component may reflect all elementscorresponding to the sport. In other words, it can be expressed that thesecond controller 2002 may extract the principal component, reflectingvarious elements of the sport.

Also, the second controller 2002 may extract at least one principalcomponent in consideration of an index to be evaluated. For example,when an index to be evaluated from heatmap data of a target player is atactical tendency of a target player, the second controller 2002 mayextract a principal component such that a weight value extracted by theprincipal component reflects the tactical tendency of the target player.This will be described in detail later.

After the step of extracting the principal component (S3200), the dataanalysis device 2000 may acquire a feature index on the basis of theextracted principal component. Specifically, the second controller 2002may acquire a feature index of the sport heatmap data corresponding tothe principal component on the basis of the principal componentextracted from the sport heatmap data.

Here, the feature index may refer to a weight value of the sport heatmapdata for each principal component so as to express the sport heatmapdata through the principal components. Also, the feature index may referto a feature vector for the principal component. That is, the featureindex may be expressed in the form of a feature vector.

The principal component of the sport heatmap data and the feature indexwill be described in more detail below.

After the step of acquiring the feature index (S3400), the data analysisdevice 2000 may evaluate the target entity on the basis of the acquiredfeature index. Specifically, the second controller 2002 may evaluate thetarget entity on the basis of the feature index of the target entity inconsideration of the characteristics of the target entity. Here, thetarget entity evaluation method may vary depending on the target entity.The evaluation of the target entity will be described in detail below.

The above-described steps of the data analysis method performed by thedata analysis device 2000 are not necessary, and some of the steps maybe omitted. That is, for example, the data analysis device 2000 may omitthe step of extracting the principal component and then acquire thefeature index of the target player using a prestored principalcomponent. As described above, the methods performed by the dataanalysis device 2000 according to this embodiment may be performedindividually or in combination, and some of the methods may be omitted.

Another example of a schematic analysis method for corrected heatmapdata performed by the data analysis device 2000 will be described below.

FIG. 19 is a flowchart showing another example of a method of a dataanalysis device analyzing data using corrected heatmap data.

The method of analyzing corrected heatmap data according to anembodiment, which is performed by the data analysis device 2000, mayinclude acquiring a feature index for a predetermined principalcomponent from the heatmap data (S4200) and evaluating a target entityon the basis of the acquired feature index (S4400).

According to an embodiment, the data analysis device 2000 may acquire afeature index of a target player from sport heatmap data of the targetplayer on the basis of a principal component pre-acquired from sportheatmap data related to at least one athlete (S4200). Specifically, thesecond controller 2002 may acquire a feature index of a target playerfrom sport heatmap data of the target player on the basis of a principalcomponent pre-extracted from heatmap data of at least one athlete whichis stored in the memory 2200. Here, the pre-acquired principal componentmay be acquired from a plurality of players through a scheme similar tothat described above. Also, the pre-acquired principal component will bedescribed in detail below.

Subsequently, the data analysis device 2000 may evaluate the targetplayer on the basis of the feature index of the target player (S4400).Specifically, the second controller 2002 may evaluate the target playerby analyzing the feature index of the target player on the basis of afeature index of at least one athlete which is prestored in the memory2200.

Several examples of the method of the data analysis device 2000analyzing the corrected heatmap data have been outlined.

Sub-methods included in the schematic analysis method performed by thedata analysis device 2000 will be described in detail below withreference to the drawing.

As described above with reference to FIG. 19 , the data analysis device2000 may prestore principal components extracted from a plurality ofpieces of heatmap data. In this regard, a method of the data analysisdevice 2000 extracting principal components on the basis of a pluralityof pieces of pre-acquired heatmap data will be described first.

FIG. 20 is related to a method of the data analysis device 2000extracting principal components from a plurality of pieces of heatmapdata according to an embodiment.

According to an embodiment, the data analysis device 2000 may extract aprincipal component common to a plurality of heatmaps (hereinafterreferred to as a common principal component) from a plurality of piecesof pre-acquired heatmap data. Specifically, the second controller 2002may extract a principal component common to a plurality of pieces ofheatmap data on the basis of a plurality of pieces of sport heatmap datarelated to a plurality of players.

Here, the second controller 2002 may combine the plurality of pieces ofsports heatmap data and use the combination as a basis for extractingcommon principal components. For example, the plurality of pieces ofheatmap data may be summed up and used as a basis for extracting commonprincipal components. As another example, an average value of theplurality of pieces of heatmap data may be used as a basis forextracting common principal components. It will be understood that aplurality of pieces of heatmap data may be combined with each otherthrough various methods in addition to the method mentioned by way ofexample.

Also, here, the second controller 2002 may cluster the plurality ofpieces of sports heatmap data in various ways and use the cluster as abasis for extracting common principal components. As an example, theplurality of pieces of sport heatmap data may be clustered inconsideration of the characteristics of an athlete. Here, thecharacteristics of the athlete may vary as described above. That is, asa specific example, the second controller 2002 may extract commonprincipal components on the basis of sport heatmap data related to onlya plurality of players having a first characteristic, may extract commonprincipal components on the basis of sport heatmap data related to onlya plurality of players having a second characteristic, or may extractcommon principal components on the basis of sport heatmap data relatedto only a plurality of players having both of the first characteristicand the second characteristic.

Also, here, the data analysis device 2000 may extract a principalcomponent for evaluating a specific ability of a player from theplurality of pieces of heatmap data. Specifically, the second controller2002 may extract, from a plurality of sport heatmap of a plurality ofplayers, a common principal component determined to extract a featureindex capable of reflecting a specific movement characteristic of aplayer. That is, the extracted common principal component may have itsown meaning, and the meaning of a feature index acquired from the sportheatmap data according to the form of the extracted common principalcomponent may also be changed. This means that a player evaluationmethod may also be diversified depending on a scheme of extracting acommon principal component, and the meaning of the common principalcomponent will be described in detail below.

Also, the common principal component may include a plurality of commonprincipal components. Specifically, as the common principal component,at least one principal component having the smallest variance may beextracted from the principal components of the plurality of pieces ofheatmap data.

Also, the number of common principal components may correspond to thetype of heatmap data. As a specific example, six common components forlocation heatmap data of a plurality of players may be extracted, andfour common components for speed heatmap data thereof may be extracted.

The method of the data analysis device 2000 extracting common principalcomponents on the basis of a plurality of pieces of heatmap data hasbeen described above. A method of the data analysis device 2000extracting common principal components on the basis of a plurality ofprincipal components will be described below.

FIG. 21 is a flowchart showing a method of a data analysis deviceprocessing principal components acquired from a plurality of pieces ofheatmap data according to an embodiment.

Referring to FIG. 21 , the data analysis device 2000 may extractprincipal components from a plurality of pieces of heatmap data of aplurality of players. Specifically, the second controller 2002 mayextract principal components from pre-acquired sport heatmap data of aplurality of players.

Subsequently, the data analysis device 2000 may analyze a plurality ofthe extracted principal components and extract a common principalcomponent including a component common to at least some of the pluralityof principal components. Specifically, the second controller 2002 mayextract a principal component common to the plurality of principalcomponents from the plurality of principal components extracted from theplurality of pieces of sport heatmap data of the plurality of players.

In addition, various methods capable of extracting common principalcomponents are similar to those described above, and thus a detaileddescription thereof will be omitted.

A method of the data analysis device 2000 extracting a principalcomponent from a plurality of pieces of sport heatmap data inconsideration of the characteristics of a sport object will be describedbelow with reference to the drawing.

FIG. 22 shows a method of the data analysis device 2000 extractingprincipal components in consideration of the characteristics of anathlete according to an embodiment.

Referring to FIG. 22 , first, the data analysis device 2000 may acquirea plurality of heatmap data sets labeled with the characteristics of asport object. Specifically, the second controller 2002 may acquire aplurality of pieces of sport heatmap data labeled with thecharacteristics of a sport object. Here, the characteristics of thesport object may be similar to those described above.

When the plurality of heatmap datasets labeled with the characteristicsare acquired, the data analysis device 2000 may extract a principalcomponent corresponding to each characteristic and store the extractedprincipal component. Specifically, the second controller 2002 mayextract principal components by combining a plurality of pieces of sportheatmap data labeled with the same characteristic and may analyze theplurality of principal components extracted from the plurality of piecesof sport heatmap data labeled with the same characteristic to extract acommon principal component for each characteristic.

When the common principal component corresponding to each characteristicis extracted, the data analysis device 2000 may acquire a feature indexof an athlete on the basis of the common principal componentcorresponding to each characteristic. Specifically, by using a commonprincipal component corresponding to a characteristic that the athletemay have, the second controller 2002 may acquire a feature indexcorresponding to the athlete's characteristic.

The second controller 2002 may store a plurality of acquired featureindices in the memory 2200.

A plurality of feature indices labeled with an athlete's characteristicmay be used as data for evaluating a feature index of a target player,as described below.

That is, the data analysis device 2000 according to an embodiment mayaccurately evaluate a target player by acquiring a feature indexcorresponding to the target player's characteristic and performingevaluation in consideration of the target player's characteristic.

The method of the data analysis device 2000 extracting common principalcomponents or principal components reflecting characteristics from aplurality of pieces of sport heatmap data has been described above.

However, according to an embodiment of the present disclosure, it ispossible to acquire a plurality of feature indices on the basis ofprincipal components not reflecting the characteristics of the sportobject and cluster a plurality of players by characteristic on the basisof the plurality of acquired feature indices.

FIG. 23 shows a method of a data analysis device clustering sportobjects by characteristic on the basis of a plurality of feature indicesaccording to an embodiment.

Referring to FIG. 23 , the data analysis device 2000 may extractprincipal components from a dataset including a plurality of pieces ofheatmap data. Specifically, the second controller 2002 may extractcommon principal components on the basis of a plurality of pieces ofsport data acquired from a plurality of athletes. Here, the method ofextracting common principal components is as described above.

When the common principal components are extracted, the data analysisdevice 2000 may compute feature indices of the plurality of players.Specifically, the second controller 2002 may acquire feature indices ofthe plurality of players by analyzing sport heatmap data of each of theplurality of players on the basis of the extracted common principalcomponents.

When the feature indices of the plurality of players are acquired, thedata analysis device 2000 may analyze and cluster a plurality of thefeature indices. In detail, the second controller 2002 may cluster theplurality of feature indices according to a predetermined criterion.Here, the predetermined criterion may vary. As an example, thepredetermined criterion may be similarity between feature indices. Asanother example, the predetermined criterion may be similarity of afeature index of a specific principal component.

Subsequently, the data analysis device 2000 may store the clustered datain the memory 2200.

The meaning of the extracted principal component or the extracted commonprincipal component will be described in detail below with reference tothe drawing.

FIGS. 24 and 25 show exemplary common principal components according toan embodiment.

First, FIG. 24 shows exemplary location heatmap data-related commonprincipal components according to an embodiment.

Referring to FIG. 24 , common principal components extracted from aplurality of pieces of location heatmap data pre-acquired from aplurality of players are shown.

Also, the data analysis device 2000 may evaluate a target player on thebasis of a feature index corresponding to a location heatmapdata-related common principal component. Specifically, the secondcontroller 2002 may evaluate a target player's athletic ability using afeature index of the target player extracted based on a location heatmapdata-related common principal component having a specific meaning. Here,the target player's athletic ability may refer to everything to beevaluated in a sport match, such as an athlete's tactical movement,personal athletic ability, etc.

As described above, the common principal components acquired from theplurality of players may have their own meanings, and a feature index ofthe target player extracted based on a corresponding common principalcomponent according to the meaning of the common principal component mayreflect the meaning of the corresponding common principal component.

(a) Line-Driven Play

Six common principal components illustrated in the drawing are onlyexamples. Therefore, it will be understood that the present disclosureis not limited to these examples and more than six or less than sixcommon principal components may be extracted.

First, referring to 24A, a common principal component with a meaning fordetermining a characteristic related to a tendency to mainly play on theside (hereinafter referred to as “side play”) is shown.

The common principal component expressed as side play shows that alocation occupancy frequency is high on one side of a stadium. A targetplayer-related feature index acquired based on the side-play commonprincipal component may be a basis for evaluating the target player'sside play. Specifically, when a result of analyzing location heatmapdata of the target player is that the feature index for the side play ismeasured as being high, this may mean that the target player has a highproportion of side play in the match where the location heatmap data ismeasured.

Also, referring to 24B, a common principal component for determining acharacteristic related to a play with an offense tendency (hereinafterreferred to as “offense play”) is shown.

The common principal component expressed as offense play shows that alocation occupancy frequency is high in an offense side part of thestadium. A target player-related feature index acquired based on theoffense-play common principal component may be a basis for evaluatingthe target player's offense play. Specifically, when a result ofanalyzing location heatmap data of the target player is that the featureindex for the offense play is measured as being high, this may mean thatthe target player has a high proportion of offense play in the matchwhere the location heatmap data is measured.

Also, referring to 24C, a common principal component for determining acharacteristic related to a tendency to play in a region other than thecenter (hereinafter referred to as “center avoidance play”) is shown.

The common principal component reflecting the center avoidance playtendency shows that a location occupancy frequency is low in the centralregion of the stadium and high in regions other than the central region.A target player-related feature index acquired based on the commonprincipal component representing this tendency may be a basis forevaluating the target player's tactical tendency. Specifically, when aresult of analyzing location heatmap data of the target player is thatthe feature index for the center avoidance play is measured as beinghigh, it may be evaluated that the corresponding player has acharacteristic of playing primarily outside midfield.

Also, referring to 24D, a common principal component for determining acharacteristic related to a tendency to play near a goalpost(hereinafter referred to as “poacher play”) is shown.

The common principal component reflecting the poacher play tendencyshows that a location occupancy frequency is dense near an offense sidegoalpost. A target player-related feature index acquired based on thecommon principal component representing this tendency may be useful inevaluating a target player who serves as a forward. A target player witha feature index for the poacher play tendency measured as being high maybe evaluated as having a characteristic of mainly aiming to shoot for agoal in front of a goalpost rather than high activity.

Also, referring to 24E, a common principal component for determining acharacteristic related to a switching play tendency is shown.

The common principal component reflecting the switching play tendencyshows that a location occupancy frequency is dense in a diagonaldirection of the stadium. A target player-related feature index acquiredbased on the common principal component representing this tendency maybe used to determine whether to tactically perform a switching play.

Last, referring to 24F, a common principal component for determining acharacteristic related to a tendency to prefer to play in a penalty box(hereinafter referred to as “box preference play”) is shown.

The common principal component reflecting the box preference play showsthat a location occupancy frequency is dense near a penalty box of thestadium. A target player-related feature index acquired based on thecommon principal component representing this tendency may be used todetermine whether to tactically prefer to play in the penalty box.

In addition to the location heatmap data-related common principalcomponents described above as examples, common principal componentshaving various meanings for evaluating the characteristics or tendenciesof an athlete may be extracted by the data analysis device 2000. It willbe understood that embodiments that are related to various commonprincipal components but not shown in the drawings are also included aspart of the present disclosure.

An exemplary dynamic heatmap data-related common principal componentwill be described below with reference to the drawing.

FIG. 25 shows exemplary dynamic heatmap data-related common principalcomponents according to an embodiment.

Referring to FIG. 25 , the data analysis device 2000 may evaluate atarget player on the basis of a feature index corresponding to a dynamicheatmap data-related common principal component. Specifically, thesecond controller 2002 may evaluate a target player's athletic abilityusing a feature index of the target player extracted based on a dynamicheatmap data-related common principal component having a specificmeaning. Here, the target player's athletic ability may refer toeverything to be evaluated in a sport match, such as an athlete'stactical movement, personal athletic ability, etc.

Like the above-described location heatmap data-related common principalcomponent, the dynamic heat map data-related common principal componentmay also have a meaning reflecting the tendency or characteristics of aplayer.

First, referring to 25A, a common principal component representing atendency to perform a movement in an offense-defense direction of astadium (hereinafter referred to as “end-to-end”) is shown. Theend-to-end-related common principal component shows that a velocityfrequency distribution for the movement in the offense-defense directionof the stadium is dense. A feature index extracted based on theend-to-end-related common principal component may reflect a player'stendency for the movement in the offense/defense direction of thestadium.

Also, referring to 25B, a common principal component representing atendency to perform a movement in a diagonal direction of the stadium(hereinafter referred to as “diagonal movement”) is shown. The diagonalmovement tendency-related common principal component shows that avelocity frequency distribution for the movement in the diagonaldirection of the stadium is dense. A feature index extracted based onthe diagonal movement tendency-related common principal component mayreflect a player's tendency for the movement in the diagonal directionof the stadium.

Also, referring to 25C, a common principal component representing atendency to perform a movement in a side direction of the stadium(hereinafter referred to as “side movement”) is shown. The side movementtendency-related common principal component shows that a velocityfrequency distribution for the movement in the side direction of thestadium is dense. A feature index extracted based on the side movementtendency-related common principal component may reflect a player'stendency for the movement in the side direction of the stadium.

Also, referring to 25D, a common principal component representing atendency to perform a high-speed movement (hereinafter referred to as“fast movement”) is shown. The fast movement tendency-related commonprincipal component shows that a speed frequency distribution for thehigh-speed movement is dense irrespective of the direction. A featureindex extracted based on the fast movement tendency-related commonprincipal component may reflect a player's tendency for the fastmovement.

As described above, a principal component extracted from heatmap datamay reflect a tendency for the characteristics of a sport object or anability to be evaluated.

Also, a principal component may reflect corresponding sportmatch-specific internal factors. For example, a principal component mayreflect match-specific internal factors such as an athlete's offensesituation. As another example, a principal component may reflectmatch-specific internal factors such as an athlete's defense situation.

As described above, the player evaluation method performed by the dataanalysis device 2000 according to an embodiment reflects in detail thecharacteristics of the target player and the tendency for an ability tobe evaluated, and thus a more comprehensive and detailed evaluation maybe possible.

Also, only some principal components have been exemplarily describedherein, but it is obvious that various examples that can be derived fromthe spirit of the present disclosure are also incorporated into thepresent disclosure.

The exemplary principal components have been described above withreference to the drawing. A feature index extracted based on a principalcomponent will be described in detail with reference to the drawing.

FIG. 26 shows an example of an acquired feature index according to anembodiment.

Referring to FIG. 26 , the data analysis device 2000 may acquire afeature index from sport heatmap data on the basis of the principalcomponent. Specifically, the second controller 2002 may acquire afeature index of a target player on the basis of a principal componentpre-acquired from a plurality of players. Also, the second controller2002 may analyze sport heatmap data of the target player to extract aprincipal component and may acquire the feature index of the targetplayer on the basis of the extracted principal component.

Also, here, the feature index may include both of a locationdata-related feature index and a dynamic data-related feature index, asdescribed above.

Referring to FIG. 25 again, an exemplary location-specific principalcomponent-related feature index and an exemplary dynamic principalcomponent-related feature index are shown.

As shown in the drawing, a feature index corresponding to each principalcomponent may be extracted. Also, a feature index may be extracted foreach match. As an example, feature indices shown in the drawing includesix location feature indices extracted for six principal components oflocation heatmap data and four dynamic feature indices extracted forfour principal components of dynamic heatmap data on a match basis.

Here, as described above, each principal component may be a principalcomponent pre-acquired from a plurality of players or may be a principalcomponent acquired by analyzing heatmap data of a target player.

Also, here, although the drawing shows that a feature index is extractedfor each match, the present invention is not necessarily limitedthereto, and time criteria for acquiring the feature index may vary.That is, for example, the second controller 2002 may acquire a featureindex by analyzing heatmap data during a portion of a match time and mayalso acquire a feature index by analyzing comprehensive heatmap data ofa target player on a yearly basis.

Also, as described above, a feature index may be labeled with thecharacteristics of the sport object.

The method of analyzing heatmap data related to an athlete has beenmainly described above. However, the above-described embodiments are notnecessarily limited to players, and the method of the data analysisdevice 2000 analyzing heatmap data of a player may be applied to alltarget entities through substantially the same method.

As an example, the data analysis device 2000 may also analyze locationheatmap data obtained by combining location heatmap data of a pluralityof players. That is, the second controller 2002 may analyze all heatmapdata of all target entities as well as heatmap data of players.

For example, when the target entity is a team, the second controller2002 may perform an analysis method (e.g., the extraction of principalcomponents and the extraction of a feature index) on data obtained bycombining heatmap data of all players identified as one team. When thetarget entity includes a plurality of players who perform somepositions, the second controller 2002 may perform an analysis method(e.g., the extraction of principal components and the extraction of afeature index) on data obtained by combining heatmap data of theplurality of players.

The method of the data acquisition device 1000 acquiring sport data andthe method of the data analysis device 2000 pre-processing the acquiredsport data and analyzing the pre-processed data according to anembodiment have been described above.

A method of the data analysis device 2000 evaluating a target playeraccording to an embodiment will be described below.

According to an embodiment, the data analysis device 2000 may evaluate atarget entity. Specifically, the second controller 2002 may evaluate atarget entity on the basis of sport data acquired by the dataacquisition device 1000. More specifically, the second controller 2002may evaluate a target entity using a feature index of the target entityobtained by analyzing sport data that is acquired by the dataacquisition device 1000 and that is related to the target entity.

Here, the evaluation method performed by the data analysis device 2000may vary depending on the type of the target entity to be evaluated.This will be described in detail later.

Also, here, the meaning of evaluation should be interpretedcomprehensively. That is, the concept of evaluation in the presentdisclosure should be comprehensively interpreted in the sense ofproviding a specific index for information related to sports, and anyobject that can be provided as a specific index related to sports may besubject to evaluation in the present disclosure.

As an example, the evaluation according to an embodiment may meandetermining the type of the target entity.

As another example, the evaluation according to an embodiment may meanevaluating a target entity's sport ability. Here, the sport ability maybe interpreted as a sport-related ability. As an example, the sportability may refer to a personal athletic ability or a tacticalperformance ability. Specifically, by analyzing the individual athleticability, it is possible to determine an ability to be improved and alsoprovide information on an improvement method therefor. Also, the meaningof the evaluation in the present disclosure may include determiningwhether the target entity can replace a specific player. Also, themeaning of the evaluation in the present disclosure may includedetermining whether the target entity is suitable for a specificposition. Also, the meaning of the evaluation in the present disclosuremay include providing guidance regarding a growth direction of a youthplayer. Also, the meaning of the evaluation in the present disclosuremay include predicting a position suitable for a youth player.

Also, the evaluation may provide the target entity's ability not only asan absolute index but also as a relative index to at least one othersport object. As an example, the evaluation in the present disclosuremay also include determining the most suitable players for at least someof a sport team squad from among a plurality of players. Also, themeaning of the evaluation in the present disclosure may includepredicting the winning percentages of sport teams. Also, the meaning ofthe evaluation in the present disclosure may include determining whichformation to use depending on the opposing team. Also, the meaning ofthe evaluation in the present disclosure may include determining whichplayer should participate depending on the opposing team. Also, themeaning of the evaluation in the present disclosure may includedetermining which player should participate depending on the opposingplayer.

In addition to the above-described examples, the meaning of theevaluation according to an embodiment of the present disclosure mayinclude any method as long as the method is capable of performingdetermination on a sport played by the target entity using the featureindex of the target entity.

First, when the target entity is a target player, a method of the dataanalysis device 2000 evaluating the target player will be describedbelow.

FIG. 27 shows an exemplary method of the data analysis device 2000evaluating a target player according to an embodiment.

Referring to FIG. 27 , the method of the data analysis device 2000evaluating a target player according to an embodiment may includeacquiring a feature index of the target player (S5000), evaluating thetarget player on the basis of the feature index (S5200), and outputtingan evaluation result (S5400).

First, the data analysis device 2000 may acquire a feature index of thetarget player (S5000). This has been described in detail above, and thusa repetitive description thereof will be omitted.

When the feature index of the target player is acquired, the dataanalysis device 2000 may evaluate the target player on the basis of thefeature index (S5200). Specifically, the second controller 2002 mayevaluate the target player's sport ability using a feature indexacquired by analyzing sport heatmap data of the target player.

Here, the method of evaluating the target player on the basis of thefeature index may vary as described above.

As an example, the second controller 2002 may evaluate the target playeron the basis of at least one feature index of a plurality of athletesstored in the memory 2200. Here, the target player may or may not beincluded in the plurality of athletes. More specifically, the secondcontroller 2002 may compare the feature index of the target player to afeature index of at least one other athlete stored in the memory 2200 todetermine the type of the target player. As another example, the secondcontroller 2002 may determine whether the target player may replaceanother athlete. As another example, the second controller 2002 maycompare feature indices of a plurality of players to determine the mostsuitable substitute player for a specific player from among theplurality of players. As another example, the second controller 2002 maypredict the growth potential of a youth player. Specifically, the secondcontroller 2002 may analyze a feature index of the youth player todetermine another player having a similar feature index or determine aposition suitable for the corresponding youth player.

As another example, the second controller 2002 may evaluate the targetplayer on the basis of feature indices of a plurality of players labeledwith the same characteristics as those of the target player.Specifically, the second controller 2002 may determine the ranking ofthe athletic ability of the target player among the plurality of playerslabeled with the same characteristics as those of the target player.

In addition to the above-described examples, as described above, thesecond controller 2002 may analyze sport data of the target player andevaluate everything that can be provided as an index.

After the step of evaluating the target player (S5200), the dataanalysis device 2000 may output a result of the evaluation (S5400).Specifically, the second controller 2002 may output the evaluationresult through an output unit separately provided in the data analysisdevice 2000 and may transmit the evaluation result to an external devicethrough the second communication module 2800.

A team evaluation method performed by the data analysis device 2000 whenthe target entity is a team will be described below.

FIG. 28 shows an exemplary team evaluation method performed by a dataanalysis device according to an embodiment.

Referring to FIG. 28 , the team evaluation method may include acquiringa feature index of a target team (S6000), evaluating the target team onthe basis of the feature index (S6200), and outputting a result of theevaluation (S6400).

First, the data analysis device 2000 may acquire a feature index of atarget team (S6000). Here, the data analysis device 2000 may acquire thefeature index of the target team in a similar way to the method ofacquiring the feature index of the target player. As an example, thesecond controller 2002 may acquire the feature index of the target teamon the basis of data related to at least one player included in thetarget team. Here, the data related to at least one player included inthe target team may include, for example, sport heatmap data, a featureindex, etc. More specifically, the second controller 2002 may acquirethe feature index of the target team using data obtained by combiningheatmap data of one or more players included in the target team or mayacquire the feature index of the target team by combining featureindices of one or more players included in the target team.

Also, when feature indices of a plurality of teams are calculated, thedata analysis device 2000 may calculate the feature indices by combiningheatmap data of the plurality of teams or may compute feature indices ofthe plurality of teams by combining the feature indices of the pluralityof teams.

When the feature index of the target team is acquired, the data analysisdevice 2000 may evaluate the target team on the basis of the featureindex (S6200). Specifically, the second controller 2002 may evaluate thetarget team's sport ability using the feature index of the target team.

Here, the method of evaluating the target team on the basis of thefeature index may vary as described above.

As an example, the second controller 2002 may evaluate the target teamon the basis of a feature index of at least one sport team stored in thememory 2200. More specifically, the second controller 2002 may comparethe feature index of the target team to a feature index of at least oneother sport team stored in the memory 2200 to determine the type of thetarget team. Alternatively, the second controller 2002 may predict theranking of the target team among a plurality of sport teams.

As another example, on the basis of the feature index of the target teamand a feature index of at least one other sport team stored in thememory 2200, the second controller 2002 may compute the winningpercentage of the target team against the other team.

Here, as described above, the feature index may be extracted from dataobtained by combining heatmap data of the target team and the opposingteam. That is, the data analysis device 2000 may compute the winningpercentage of the target team against the opposing team on the basis ofdata obtained by combining the heatmap data of the target team and theother team.

In addition to the above-described examples, as described above, thesecond controller 2002 may analyze sport data of the target team andevaluate everything that can be provided as an index.

After the step of evaluating the target team (S6200), the data analysisdevice 2000 may output a result of the evaluation (S6400). Specifically,the second controller 2002 may output the evaluation result through anoutput unit separately provided in the data analysis device 2000 and maytransmit the evaluation result to an external device through the secondcommunication module 2800.

FIG. 29 shows an exemplary target entity evaluation method performed bya data analysis device according to an embodiment.

Referring to FIG. 29 , the target entity evaluation method may includeacquiring a feature index of a target team (S7000), evaluating a targetentity on the basis of the feature index (S7200), and outputting aresult of the evaluation (S7400).

First, the data analysis device 2000 may acquire a feature index of atarget entity (S7000). The target entity in the present embodimentincludes, for example, one or more players who perform a specific roleor a specific position.

The data analysis device 2000 may acquire the feature index of thetarget entity in a similar way to the method of acquiring the featureindex of the target player or the target team. As an example, the secondcontroller 2002 may acquire the feature index of the target entity onthe basis of data related to at least one player included in the targetentity. Here, the data related to at least one player included in thetarget entity may include, for example, sport heatmap data, a featureindex, etc.

More specifically, the second controller 2002 may acquire the featureindex of the target entity using data obtained by combining heatmap dataof one or more players included in the target entity or may acquire thefeature index of the target entity by combining feature indices of oneor more players included in the target entity.

Also, when a feature index of a target entity including a plurality ofplayers is calculated, the data analysis device 2000 may calculate thefeature index by combining all heatmap data related to the target entityor may compute the feature index of the target entity by combiningfeature indices of the players included in the target entity.

When the feature index of the target entity is acquired, the dataanalysis device 2000 may evaluate the target entity on the basis of thefeature index (S7200). Specifically, the second controller 2002 mayevaluate the target entity's sport ability using the feature index ofthe target entity.

Here, the method of evaluating the target entity on the basis of thefeature index may vary as described above about a target player and atarget team.

As an example, the second controller 2002 may evaluate the target entityon the basis of a feature index of at least one other sport objectstored in the memory 2200. More specifically, the second controller 2002may compare the feature index of the target entity to a feature index ofat least one other sport object stored in the memory 2200 to determinethe type of the target entity. Alternatively, the second controller 2002may predict the ability ranking of the target entity among a pluralityof sport teams.

As another example, on the basis of the feature index of the targetentity and a feature index of at least one other sport object stored inthe memory 2200, the second controller 2002 may compute the comparativeadvantage of the target entity with respect to other sports objects.

Here, as described above, the feature index may be extracted from dataobtained by combining heatmap data of the target team and at least onesport object. That is, the data analysis device 2000 may evaluate thetarget entity on the basis of the data obtained by combining the heatmapdata of the target entity and the other sport object.

In addition to the above-described examples, as described above, thesecond controller 2002 may analyze sport data of the target entity andevaluate everything that can be provided as an index.

After the step of evaluating the target entity (S7200), the dataanalysis device 2000 may output a result of the evaluation (S7400).Specifically, the second controller 2002 may output the evaluationresult through an output unit separately provided in the data analysisdevice 2000 and may transmit the evaluation result to an external devicethrough the second communication module 2800.

The step of the data analysis device 2000 evaluating the target entityhas been described above in detail.

According to an embodiment, the data analysis device 2000 may providevarious services on the basis of the evaluation result. Specifically,the second controller 2002 may receive information of an externalnetwork or an external device from the second communication module 2800and may provide a service corresponding to the evaluation result of thetarget entity on the basis of the information.

Here, the service provided based on the evaluation result of the targetentity may be variously interpreted.

As an example, the service may refer to the provision of informationrelated to the scouting of a player. As another example, the service mayrefer to the provision of information related to training and/or a matchof a professional team. As another example, the service may refer to theprovision of information related to training and/or a match of a youthteam. As another example, the service may refer to the broadcasting ofthe evaluation result. As another example, the service may refer to theprediction of a winner of a specific match or provide winning percentageinformation corresponding to each team through the evaluation result. Asanother example, the service may refer to the uploading of an evaluationresult to an online network. As another example, the service may referto the provision of the evaluation result in real time.

All methods performed by the elements of the system 100 according to theabove-described embodiments may be performed independently or incombination, and a subject that performs the methods may be changed.

The method according to an embodiment may be implemented as programinstructions executable by a variety of computers and may be recorded ona computer-readable medium. The computer-readable medium may includeprogram instructions, data files, data structures, and the like alone orin combination. The program instructions recorded on the medium may bedesigned and configured specifically for an embodiment or may bepublicly known and available to those skilled in the field of computersoftware. Examples of the computer-readable recording medium include amagnetic medium, such as a hard disk, a floppy disk, and a magnetictape, an optical medium, such as a compact disc read-only memory(CD-ROM), a digital versatile disc (DVD), etc., a magneto-optical mediumsuch as a floptical disk, and a hardware device specially configured tostore and perform program instructions, for example, a read-only memory(ROM), a random access memory (RAM), a flash memory, etc. Examples ofthe computer instructions include not only machine language codegenerated by a compiler, but also high-level language code executable bya computer using an interpreter or the like. The hardware device may beconfigured to operate as one or more software modules in order toperform operations of an embodiment and vice versa.

According to the present invention, by extracting a feature index usinga unique principal component reflecting the characteristics of acorresponding sport from sport data of an athlete, it is possible toperform an objective and detailed evaluation reflecting thecharacteristics of the sport on a player.

According to the present invention, by acquiring location data anddynamic data from an athlete and processing the location data and thedynamic data using at least one piece of heatmap data such that theprocessed data is appropriate for the analysis of a corresponding sport,it is possible to perform an evaluation with high accuracy orreliability on the athlete.

According to the present invention, it is possible to provide anobjective evaluation result for a target player on the basis of featureindices of a plurality of players who play the same sport as thatperformed by the target player.

According to the present invention, by analyzing the characteristics ofa sport played by a target player, extracting meaningful data from thesport, and evaluating the target player, it is possible to provide anaccurate evaluation result.

Although the present invention has been described with reference tospecific embodiments and drawings, it will be appreciated that variousmodifications and changes can be made from the disclosure by thoseskilled in the art. For example, appropriate results may be achievedalthough the described techniques are performed in an order differentfrom that described above and/or although the described components suchas system, a structure, a device, or a circuit are combined in a mannerdifferent from that described above and/or replaced or supplemented byother components or their equivalents.

Therefore, other implementations, embodiments, and equivalents arewithin the scope of the following claims.

What is claimed is:
 1. A method for evaluating an athlete, the methodcomprising: obtaining, by a controller, a location data for a pluralityof sports participants, the location data indicating a location of theplurality of sports participants in a playground during a sports game;calculating, by the controller, a reference location data for a specificparticipant, wherein the reference location data is obtained by usingthe location data of at least two other sports participants playing thesame sports game with the specific participant; generating, by thecontroller, a relative location data for the specific participant basedon the location data for the specific participant and the referencelocation data, the relative location data for the specific participantindicating a relative location of the specific participant respect tothe reference location data; generating, by the controller, a relativeheatmap data for the specific participant using the relative locationdata for the specific participant, wherein the relative heatmap dataincludes a relative location heatmap of the specific participant, therelative location heatmap reflecting a frequency of a spatial occupancyof the relative location of the specific participant during the sportsgame; and calculating, by the controller, a performance index for thespecific participant from the relative heatmap data for the specificparticipant, wherein the performance index represents weight values ofthe relative heatmap data of the specific participant according toprincipal components of a plurality of relative heatmap data for aplurality of athletes playing the same sports game with the specificparticipant.
 2. The method according to claim 1, wherein the principalcomponent is configured to minimize a variance of a plurality oflocation heatmap data for a plurality of athletes playing the samesports game with the specific participant.
 3. The method according toclaim 1, wherein the reference location data is an average location of aplurality of sports participants playing in a same team as the specificparticipant.
 4. The method according to claim 1, wherein the relativelocation data is obtained by multiplying a predetermined value to thelocation data for the specific participant.
 5. The method according tothe claim 4, wherein the location data is defined by a 2-dimensionalcoordinate system including an offence direction value and a transversedirection value, which is perpendicular to the offence direction value,and wherein the relative location data is obtained by multiplying thepredetermined value only to the offence direction value or to thetransverse direction value.
 6. The method according to the claim 1,further comprising displaying the relative location heatmap; wherein therelative location heatmap has a visual indication which reflects anamount of the frequency of the spatial occupancy of the relativelocation of the specific participant during the sports game.
 7. A methodfor evaluating an athlete using a multi-heatmap analysis, the methodcomprising: obtaining, by a controller, a location data indicating alocation of a sports participant in a playground during a sports game;obtaining, by the controller, a dynamics data, wherein the dynamics dataincludes at least one of a velocity data, an acceleration data and ajerk data, each indicating a velocity, an acceleration and a jerk of thesports participant, respectively; generating a first heatmap data for alocational heatmap reflecting a frequency of a spatial occupancy of thelocation of the sports participant during the sports game; generating asecond heatmap data for a dynamics heatmap reflecting a frequencydistribution of one of the velocity, the acceleration, and the jerk ofthe sports participant during the sports game; calculating a performanceindex from the first heatmap data and the second heatmap data, whereinthe performance index includes a first performance index representingweight values of the first heatmap data of the sports participantaccording to first principal components of a plurality of first heatmapdata for a plurality of athletes playing the same sports game with thesports participant, and a second performance index representing weightvalues of the second heatmap data of the sports participant according tosecond principal components of a plurality of second heatmap data forthe plurality of athletes; and evaluating the sports participant basedon the performance index.
 8. The method according to claim 7, whereinthe first principal components indicate a predetermined tactical spatialoccupancy performed by the sports participant during the gameplay. 9.The method according to claim 8, wherein the predetermined tacticalspatial occupancy comprises at least one of a side-play, an attackingplay, a diagonal play or a box-to-box play.
 10. The method according tothe claim 7, wherein the second principal components indicate apredetermined tactical movement performed by the sports participantduring the gameplay.
 11. The method according to the claim 10, whereinthe predetermined tactical movement comprises at least one of afront-back run, a diagonal run, a lateral run or a high-speed run. 12.The method according to the claim 7, wherein the performance indexindicates a specific play style among a plurality of play stylescategorized from the plurality of performance indexes obtained from theplurality of sports participants in the same sports.
 13. A method forevaluating an athlete, the method comprising: obtaining, by acontroller, a locational data indicating a location of a sportsparticipant in a playground during a sport game; obtaining, by thecontroller, a play-position information including a first positionidentifier indicating that the sports participant plays a firstplay-position and a second position identifier indicating that thesports participant plays a second play-position, the first play-positionand the second play-position being different to each other, and whereinthe first play-position and the second play-position are defined asroles performed by the sports participant during the same sports game;generating, by the controller, based on a first portion of thelocational data corresponding to the first position identifier, a firstheatmap data for a first heatmap reflecting a frequency of a spatialoccupancy of the location of the sports participant of playing the firstplay-position during the same sports game; generating, by thecontroller, based on a second portion of the locational datacorresponding to the second position identifier, a second heatmap datafor a second heatmap reflecting a frequency of a spatial occupancy ofthe location of the sports participant of playing the secondplay-position during the same sports game; and generating, by thecontroller, an evaluation information for the sports participant,wherein the evaluation including a first performance index which isacquired from the first heatmap data and is labeled with the firstplay-position, and a second performance index which is acquired from thesecond heatmap data and is labeled with the second play-position. 14.The method according to claim 13, receiving, by the controller, amessage requesting a performance evaluation of the sports participant asa sports player of a target play-position, the target play-positionbeing one of the first play-position and the second play-position; andoutputting, by the controller, an evaluation of the sports participantbased on one of the first heatmap data and the second heatmap data,corresponding to the target play-position.
 15. A method for analyzingperformance of a sports participant, the method comprising: receiving,by a controller, a GPS(GNSS) data from a target participant in a sportsgame, wherein the GPS data reflects a position of the target sportsparticipant during the sports game, and wherein the GPS data includes alocation data; generating, by the controller, a location heatmap usingthe location data of the target sports participant, wherein the locationheatmap, includes a first plurality of unit cells corresponding to apredetermined region of interest, wherein the predetermined region ofinterest is at least part of an entire region of playable area of thetarget sports participant when the target sports participant is playingin the sports game, and wherein each of the first plurality of unitcells indicates a first intensity related to an amount of the locationdata in a first unit cell; generating, by the controller, a velocityheatmap using a velocity data of the target sports participant, whereinthe velocity heatmap includes a second plurality of unit cells forming atwo dimensional matrix of which axes of the two dimensional matrix aredefined by 1) a first velocity element of a movement velocity of thetarget player along a lengthwise direction of the predetermined regionof interest, and 2) a second velocity element of the movement velocityalong a widthwise direction of the predetermined region of interest andwherein each of the second plurality of unit cells indicates a secondintensity related to an amount of a velocity data above a threshold; andobtaining, by the controller, a feature matrix using a locationeigen-map and a velocity eigen-map based on the location heatmap and thevelocity heatmap, wherein the location eigen-map is obtained from thelocation heatmaps and is configured to minimize a variance of locationheatmaps comprising the location data of a plurality of sportsparticipants, and wherein the velocity eigen-map is obtained from thevelocity heatmaps and is configured to minimize a variance of velocityheatmaps comprising the velocity data of the plurality of sportsparticipants.
 16. The method according to claim 15, wherein the locationeigen-map indicates a play-style of the sports participant.
 17. Themethod according to claim 15, wherein the velocity eigen-map indicates aplay-style of the sports participant.
 18. The method according to claim15, further comprising: evaluating, by the controller, the sportsparticipant based on the feature matrix, wherein the evaluating is todetermine a feature matrix similar to the feature matrix of the sportsparticipant among feature matrices which are previously obtained from aplurality of sports participants playing the same sports game as thesports participant.
 19. The method according to claim 15, the locationeigen-map and the velocity eigen-map reflect an offence situation or adefense situation in the sports game.